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Collect data on any topic of interest to you, preferably something related to your work (application of the techniques from this course to your work will add value to your organization and to you.) You may choose to do either Time Series Forecasting or Causal Forecasting.Time Series ForecastingPick a variable you want to forecast, and collect data on its values in the past (at least 25 time periods, preferably closer to 50). The data should NOT be annual, since that will prevent the study of seasonality, if any. Choose monthly or quarterly or daily or hourly data.Create a scatter plot showing the data over time. Discuss what approach is most appropriate for forecasting.Regardless of the above, forecast using all the methods discussed in class – Naïve, Moving Average, Exponential Smoothing, Regression, and Classical Decomposition.Compare all methods using appropriate evaluation criteria (Bias, MAD, MAPE, MSE, Standard Error) ORCausal ForecastingYou must have at least 5 independent variables (can be a mix of categorical and numeric) and a dependent variable (numeric). The number of observations will depend on the circumstances, but in general, the more the better – rule of thumb is to get at least 10 times as many observations as variables (so for 5 variables, you need 50 observations).For each variable, show distribution of observations with frequency charts, mean and standard deviation computations, etc.Show relationships of each independent variable individually with the dependent using scatter plots.Perform regression analysis to show overall model for predicting the value of the dependent. Remember to eliminate variables that are not significant, and run regression analysis multiple times until your model has only significant variables, or until you conclude that nothing is significant.Interpret the results and write a report. The report must first briefly describe the background, what you are trying to predict, what the variables are, and how you collected the data, before showing the analyses and results. The report must stand alone – one should be able to understand the salient points of everything you did without having to look at your spreadsheet.Report FormatIntroduction: What motivates this study? Who is it important to? Provide general backgroundData: How much data was collected? Number of Observations, the variable(s), the way the variables were measured, the source of the dataPreliminary Analysis: Scatter Plot(s), and interpretation of the plots. For time series, what does the plot tell you about the relationships and the method of forecasting that might work best? For causal forecasting, draw multiple plots, and interpret each. How does each X seem to relate to Y?Forecasting: Forecast Y using all the methods. Draw graphs of actual vs. forecasted value.Evaluation: Compute Errors, compute Bias and at least one of MAD, MAPE, MSE (SE). Compare the values across methods.Conclusion: What is the forecast for the next time period using the best of the methods for this data?

Collect data on any topic of interest to you, preferably something related to your work (application of the techniques from this course to your work will add value to your organization and to you.) You may choose to do either Time Series Forecasting or Causal Forecasting.Time Series ForecastingPick a variable you want to forecast, and collect data on its values in the past (at least 25 time periods, preferably closer to 50). The data should NOT be annual, since that will prevent the study of seasonality, if any. Choose monthly or quarterly or daily or hourly data.Create a scatter plot showing the data over time. Discuss what approach is most appropriate for forecasting.Regardless of the above, forecast using all the methods discussed in class – Naïve, Moving Average, Exponential Smoothing, Regression, and Classical Decomposition.Compare all methods using appropriate evaluation criteria (Bias, MAD, MAPE, MSE, Standard Error) ORCausal ForecastingYou must have at least 5 independent variables (can be a mix of categorical and numeric) and a dependent variable (numeric). The number of observations will depend on the circumstances, but in general, the more the better – rule of thumb is to get at least 10 times as many observations as variables (so for 5 variables, you need 50 observations).For each variable, show distribution of observations with frequency charts, mean and standard deviation computations, etc.Show relationships of each independent variable individually with the dependent using scatter plots.Perform regression analysis to show overall model for predicting the value of the dependent. Remember to eliminate variables that are not significant, and run regression analysis multiple times until your model has only significant variables, or until you conclude that nothing is significant.Interpret the results and write a report. The report must first briefly describe the background, what you are trying to predict, what the variables are, and how you collected the data, before showing the analyses and results. The report must stand alone – one should be able to understand the salient points of everything you did without having to look at your spreadsheet.Report FormatIntroduction: What motivates this study? Who is it important to? Provide general backgroundData: How much data was collected? Number of Observations, the variable(s), the way the variables were measured, the source of the dataPreliminary Analysis: Scatter Plot(s), and interpretation of the plots. For time series, what does the plot tell you about the relationships and the method of forecasting that might work best? For causal forecasting, draw multiple plots, and interpret each. How does each X seem to relate to Y?Forecasting: Forecast Y using all the methods. Draw graphs of actual vs. forecasted value.Evaluation: Compute Errors, compute Bias and at least one of MAD, MAPE, MSE (SE). Compare the values across methods.Conclusion: What is the forecast for the next time period using the best of the methods for this data?

Collect data on any topic of interest to you, preferably something related to your work (application of the techniques from this course to your work will add value to your organization and to you.) You may choose to do either Time Series Forecasting or Causal Forecasting.Time Series ForecastingPick a variable you want to forecast, and collect data on its values in the past (at least 25 time periods, preferably closer to 50). The data should NOT be annual, since that will prevent the study of seasonality, if any. Choose monthly or quarterly or daily or hourly data.Create a scatter plot showing the data over time. Discuss what approach is most appropriate for forecasting.Regardless of the above, forecast using all the methods discussed in class – Naïve, Moving Average, Exponential Smoothing, Regression, and Classical Decomposition.Compare all methods using appropriate evaluation criteria (Bias, MAD, MAPE, MSE, Standard Error) ORCausal ForecastingYou must have at least 5 independent variables (can be a mix of categorical and numeric) and a dependent variable (numeric). The number of observations will depend on the circumstances, but in general, the more the better – rule of thumb is to get at least 10 times as many observations as variables (so for 5 variables, you need 50 observations).For each variable, show distribution of observations with frequency charts, mean and standard deviation computations, etc.Show relationships of each independent variable individually with the dependent using scatter plots.Perform regression analysis to show overall model for predicting the value of the dependent. Remember to eliminate variables that are not significant, and run regression analysis multiple times until your model has only significant variables, or until you conclude that nothing is significant.Interpret the results and write a report. The report must first briefly describe the background, what you are trying to predict, what the variables are, and how you collected the data, before showing the analyses and results. The report must stand alone – one should be able to understand the salient points of everything you did without having to look at your spreadsheet.Report FormatIntroduction: What motivates this study? Who is it important to? Provide general backgroundData: How much data was collected? Number of Observations, the variable(s), the way the variables were measured, the source of the dataPreliminary Analysis: Scatter Plot(s), and interpretation of the plots. For time series, what does the plot tell you about the relationships and the method of forecasting that might work best? For causal forecasting, draw multiple plots, and interpret each. How does each X seem to relate to Y?Forecasting: Forecast Y using all the methods. Draw graphs of actual vs. forecasted value.Evaluation: Compute Errors, compute Bias and at least one of MAD, MAPE, MSE (SE). Compare the values across methods.Conclusion: What is the forecast for the next time period using the best of the methods for this data?

Collect data on any topic of interest to you, preferably something related to your work (application of the techniques from this course to your work will add value to your organization and to you.) You may choose to do either Time Series Forecasting or Causal Forecasting.Time Series ForecastingPick a variable you want to forecast, and collect data on its values in the past (at least 25 time periods, preferably closer to 50). The data should NOT be annual, since that will prevent the study of seasonality, if any. Choose monthly or quarterly or daily or hourly data.Create a scatter plot showing the data over time. Discuss what approach is most appropriate for forecasting.Regardless of the above, forecast using all the methods discussed in class – Naïve, Moving Average, Exponential Smoothing, Regression, and Classical Decomposition.Compare all methods using appropriate evaluation criteria (Bias, MAD, MAPE, MSE, Standard Error) ORCausal ForecastingYou must have at least 5 independent variables (can be a mix of categorical and numeric) and a dependent variable (numeric). The number of observations will depend on the circumstances, but in general, the more the better – rule of thumb is to get at least 10 times as many observations as variables (so for 5 variables, you need 50 observations).For each variable, show distribution of observations with frequency charts, mean and standard deviation computations, etc.Show relationships of each independent variable individually with the dependent using scatter plots.Perform regression analysis to show overall model for predicting the value of the dependent. Remember to eliminate variables that are not significant, and run regression analysis multiple times until your model has only significant variables, or until you conclude that nothing is significant.Interpret the results and write a report. The report must first briefly describe the background, what you are trying to predict, what the variables are, and how you collected the data, before showing the analyses and results. The report must stand alone – one should be able to understand the salient points of everything you did without having to look at your spreadsheet.Report FormatIntroduction: What motivates this study? Who is it important to? Provide general backgroundData: How much data was collected? Number of Observations, the variable(s), the way the variables were measured, the source of the dataPreliminary Analysis: Scatter Plot(s), and interpretation of the plots. For time series, what does the plot tell you about the relationships and the method of forecasting that might work best? For causal forecasting, draw multiple plots, and interpret each. How does each X seem to relate to Y?Forecasting: Forecast Y using all the methods. Draw graphs of actual vs. forecasted value.Evaluation: Compute Errors, compute Bias and at least one of MAD, MAPE, MSE (SE). Compare the values across methods.Conclusion: What is the forecast for the next time period using the best of the methods for this data?

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